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The radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure. Plain Language Summary The radius of maximum wind (RMW) is a key structural parameter of tropical cyclones that describes how far the strongest winds are from the storm's center. The RMW is closely tied to significant hazards such as wind, storm surge, and rainfall. However, little forecast guidance is provided for the RMW resulting in forecasters using climatological estimates to help communicate hazard risk. In order to better forecast the RMW, we need to understand the performance of the few guidance techniques available. We compare RMW forecasts from the Hurricane Analysis and Forecast System (HAFS) to two statistical models and a climatological estimate. Forecasts of the RMW from HAFS are not competitive with statistical derivations of the RMW with marginally better to comparable skill for stronger tropical cyclones. The results indicate that there is a strong need for future improvements to better predict tropical cyclone structure in addition to track and intensity. Key Points Forecasting the radius of maximum wind (RMW) is important for forecasting tropical cyclone hazards A RMW climatology and persistence model is created to determine forecast skill Statistical RMW forecasts are skillful and outperform dynamical model guidance

Tropical cyclones are one of the nature’s most violent manifestations and potentially the deadliest of all meteorological phenomena. It is a unique combination of violent wind, heavy rainfall, and mountainous waves in sea. The maximum sustained wind speed, minimum sea level pressure, and the radius of maximum winds are important parameters for understanding a particular tropical cyclone and to differentiate it from a depression to tropical storms. The objective of this particular paper is to identify a possible range of maximum sustained wind speed, minimum sea level pressure, and radius of maximum winds which facilitates tropical depressions to lead to tropical storms over Bay of Bengal and Arabian Sea of Indian Ocean basin. The method of rough set theory which is based on condition—decision support system is implemented for the purpose. The result reveals that the threshold ranges of the maximum sustained wind speed, minimum sea level pressure and radius of maximum winds associated with tropical depression are possible that can aid in the predictability of tropical storm over Indian Ocean. The results are validated with significant tropical storms of 2009 and 2010 observations through Doppler and satellite imageries.

This study examines the complex behaviour of tropical cyclones, specifically focusing on accurately estimating the Radius of Maximum Wind (RMW) and its implications for climate and oceanic processes. We use RMW data provided by the India Meteorological Department (IMD) to evaluate three existing methods for estimating RMW and introduce a new method based on machine learning techniques. The findings underscore the critical role of the RMW in shaping the intensity and impact of tropical cyclones, emphasizing the need for refined estimation techniques to bolster forecasting and preparedness measures. We use four cases to test our method. The results of our method show the root square mean error is 10.63 nautical miles and the error percentage of 17.00 % , which are lower than other methods. Therefore, the proposed ensemble machine learning model, designed to estimate RMW, showcases considerable promise in attaining precise RMW estimation.

The parametric typhoon model is a powerful typhoon prediction and reproduction tool with advantages in accuracy, and computational speed. To simulate typhoons’ horizontal features, the longitude and latitude of the typhoon center, central pressure, radius of maximum wind speed (Rmax), and background states (such as surface air pressure and wind speed) are required. When a typhoon approaches or is predicted to affect Korea, the Korea Meteorological Agency (KMA) notifies the above-mentioned parameters, except for the Rmax and background state. The contribution of background wind and pressure is not very significant; however, Rmax is essential for calculating typhoon winds. Therefore, the optimized Rmax for the typhoons over the past five years was estimated at each time step compared with the in situ wind observation record. After that, a fifth-order polynomial fitting was performed between the estimated Rmax and the radius of strong wind (RSW; >15 m/s) provided by the KMA. Finally, the Rmax was calculated from the RSW via the empirical equation, and the horizontal fields of typhoon Hinnamnor (2211) were reproduced using a parametric model. Furthermore, the ocean storm surge height was adequately simulated in the surge model.

The maximum significant wave height ( H s m a x ) induced by a tropical cyclone (TC) can be estimated from an empirical fetch formula using the TC’s size, maximum wind speed, and translation speed, in which larger, stronger, and faster-moving TCs generally have higher the H s m a x . In the formula, the radius of maximum wind (RMW) has been widely used as the TC size parameter under the assumption that H s m a x is mainly generated by strong winds near the RMW. This study investigates whether RMW is the optimal parameter for determining TC-induced H s m a x through extensive wave model simulations for North Atlantic hurricanes from 1988–2017. The correlation analysis between the estimated H s m a x and TC size parameters revealed that the radius of the 34-kt wind speed (R34, r = 0.84–0.95) was much higher than the widely used RMW ( r = 0.33–0.58), which suggests that R34 is a more important TC size parameter for determining TC-induced H s m a x than RMW. This result can be explained by the fact that R34 showed a significantly higher correlation ( r = 0.96) than RMW ( r = 0.31) with cumulative TC wind speeds, which are closely related to H s m a x . These findings also indicate that the TC-induced H s m a x is more affected by the region containing moderately strong winds outside the TC than by the region of maximum wind speed near the RMW. Our paper provides additional insight into the mechanisms by which extreme wave heights, which cause severe damage during TC passage, occur.

The Holland (2010) parametric wind model has been extensively utilized in tropical cyclone and storm surge-related coastal hazard mitigation and management studies. The only remaining input parameter, the radius of maximum wind speed (Rm), is usually generated by previously proposed empirical relations which are, however, sensitivity to study areas in producing better performed numerical results. In order to acquire optimal Rm formulations over the region of Zhoushan Archipelago, East China Sea, 16 empirical relations were compiled into the Holland (2010) model to produce time series of the pressure, wind speed, and wind direction in comparison to observational records taken at three stations during the tropical cyclone events of Ampil and Rumbai. Their respective agreements were evaluated by error metrices including the root mean square error, correlation coefficient, mean bias error, and scatter index, whilst the overall performances of the 16 formulations were ranked according to a proposed comprehensive error. In the following order, the Rm formulations of Lu (2012), Zhou (2005), Kato (2018), and Jiang (2008) ranked the best for both events in terms of their minimum comprehensive errors; however, recommendations on the application of specific empirical formulations for the region of Zhoushan Archipelago are also provided herein from the perspective of conservation and accuracy.

Gong, J.; Jia, X.; Zhuge, W.; Guo, W., and Lee, D., 2020. Assessment of a parametric tropical cyclone model for typhoon wind modeling in the Yellow Sea. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads.Journal of Coastal Research, Special Issue No. 99, pp. 67-73. Coconut Creek (Florida), ISSN 0749-0208. Tropical cyclones (TCs) pose one of the most dangerous threats to the lives and properties in coastal areas. Various parametric models have been developed to simulate TC wind fields and are critical for modeling and assessing the risk of storm surges and coastal inundations. The Holland parametric wind model (Holland, 1980) has been widely applied in the modeling of TC wind fields; however, systematic assessments of its performance in simulating typhoon wind fields in the Yellow Sea, where typhoons undergo extratropical transitions, are rare. Four typhoons that passed over the Yellow Sea were selected, and the wind measurements at 12 gauges and buoys along the Korean coast were used for model validation and assessment. The results showed that the Holland model is capable of producing satisfactory wind results for typhoons passing over the Yellow Sea given accurate TC parameters. The results also highlighted that the model-predicted typhoon winds were highly sensitive to Rmax and that the shape coefficient B thus should be chosen carefully when the Holland model wind fields are used for TC-induced storm surge or wave modeling.

The maximum wind speed radius of a strong typhoon making landfall is an important factor influencing the numerical forecasting of storm surges. A method for inverting the maximum wind speed radius of typhoons based on wave buoys data was designed to significantly reduce the error in 24 h storm surge forecasting in this paper, and an operation scheme was proposed to enhance the storm surge numerical forecasting system based on this method. Hangzhou Bay and the Yangtze River Estuary, which have been frequently impacted by typhoons over the past five years, were selected as the research area. Common schemes for the maximum wind speed radius were analyzed, and five ladder schemes (10, 15, 20, 25, and 30 km) were established for wave and storm surge numerical model verification of Typhoon Muifa in 2022. Based on a comparison of the wave hindcast results and wave buoys observation data, the wave hindcast result of the commonly used scheme (30 km) was significantly greater than that of the observation data, and the optimal scheme (15 km) closest to the observation data could be determined during the 48 h warning period. Moreover, it was difficult to identify the optimal scheme during the 48 h warning period based on the storm surge hindcast results. A 24 h storm surge numerical forecasting test was performed with the commonly used scheme (30 km) and the optimal scheme (15 km). The results showed that the root mean square error (RMSE) of the optimal scheme (15 km) was 34% lower than that of the commonly used scheme (30 km), while the maximum storm surge error was also reduced from 47.7% for the commonly adopted scheme (30 km) to 11.8% for the optimal scheme (15 km). The maximum storm surges under the optimal scheme (15 km) along Hangzhou Bay and the Yangtze River Estuary ranged from 1.9 to 2.2 m, which were closer to the observation data, and the maximum storm surge under the commonly used scheme (30 km) was 0.8~1.2 m greater than that under the optimal scheme (15 km).

The Shore Protection Manual published by the U.S. Army Corps of Engineers in 1984 provides hurricane wave prediction techniques. On 27 September, 1998, Hurricane Georges moved into the northeast Gulf of Mexico. During this period, a network of 5 surface stations operated by the National Data Buoy Center recorded simultaneously atmospheric pressure and significant wave height and period. In addition, both satellite and aircraft measurements were obtained. All of these data sets were employed to evaluate the prediction techniques as provided in the Shore Protection Manual. It is shown that the radius of maximum wind as determined by the surface network is in good agreement with both satellite and aircraft measurements. The predicted significant wave height and period are also consistent with buoy measurements. Finally, in order to estimate the significant wave height away from the radius of maximum wind, the graphs in the Shore Protection Manual were digitized and incorporated into a linear regression analysis. Using two buoys, one near the radius of maximum wind and the other away from it, this statistical method provides a reasonable agreement between prediction and measurement for practical applications.

Based on gradient wind equations, including frictional force, and considering the effect of the movement of a tropical cyclone on wind speed, the Fujita Formula is improved and further simplified, and the numerical scheme for calculating the maximum wind speed radius and wind velocity distribution of a moving tropical cyclone is derived. In addition, the effect of frictional force on the internal structure of the tropical cyclone is discussed. By comparison with observational data, this numerical scheme demonstrates great advantages, i.e.: it can not only describe the asymmetrical wind speed distribution of a tropical cyclone reasonably, but can also calculate the maximum wind speed in each direction within the typhoon domain much more accurately. Furthermore, the combination of calculated and analyzed wind speed distributions by the scheme is perfectly consistent with observations.